License plate recognition (LPR), also known as automatic number plate recognition (ANPR), is a mass surveillance method that performs optical character recognition on images to read vehicle license plates that display registration identifiers to uniquely identify the vehicles. This feature is becoming increasingly important in modern intelligent transportation systems. It is widely adopted for monitoring road network traffic flows, controlling moving assets, tracking stolen vehicles and identifying dangerous drivers.
At its core, LPR relies on an image processing algorithm to automatically recognize the acquired license plate image and identify the corresponding metadata (e.g., license plate number or registration identifier). However, even the most advanced LPR technique nowadays cannot guarantee 100% accuracy in recognizing vehicle license plates (less than 60% accuracy in some extreme cases). Incorrect LPR may be the result of inherent algorithm limitations, bad weather conditions, poor illumination, too-fast passing speed, blocking obstacles or LPR sensor malfunctioning. Incorrect LPRs may mislead tracking of a suspicious vehicle, which leads to invalid or untrustworthy recommendations and analysis results for higher level applications.
It is of particular interest to an LPR sensor operator to track down LPR sensors that constantly produce faulty results, which indicate that the sensor is malfunctioning and factory maintenance may be necessary. In such a scenario, all LPR records (e.g., millions of records) from all sensors are typically manually searched in order to find the sensors that are probably malfunctioning or to determine the cause of the fault. Currently, there is no better way to pick out incorrect LPR records other than manually going through all data records of interest. Manual detection of incorrect LPRs is extremely costly and problematic, particularly with the dramatic increase in the amount of data records in many city-wide systems that typically produce millions of records from hundreds of sensors daily.
Therefore, there is a need for an improved framework that addresses the above-mentioned challenges.